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Will Shelley

Senior Data Science & Analytics Leader

Executive Summary

Analytics leader with 12+ years directing revenue growth through customer insights, econometric modeling, and AI solutions. Builds high-performing teams delivering measurable business impact via machine learning and strategic segmentation. Proven ability to lead complex analytics initiatives while maintaining hands-on expertise in forecasting, causal inference, and pricing optimization across enterprise-scale operations.

Education

The University of Georgia

Master of Science, Applied Economics (MS)

Athens, GA

2016

Thesis: Understanding Household Food Waste: A Rational Inefficiency Approach

Georgia Southwestern State University

Bachelor of Science, Business Administration in Management (BBA)

Americus, GA

2013

Professional Experience

Director of Analytics – Corporate Strategy

Unum Group

Chattanooga, TN

Present - 2023

Voluntary Benefits Elasticity Modeling: Directed initiative modeling consumer propensity to purchase voluntary benefit insurance using price elasticity analysis, enabling pricing optimization that increased product uptake by 18% across target segments.
AI-Powered Internal Intelligence: Deployed Large Language Models for analyzing customer service transcripts and internal documentation, implementing governance frameworks ensuring regulatory compliance while enabling cross-team collaboration, reducing help desk calls by 30% and generating $590K OpEx savings.
Analytics Platform & Team Development: Established enterprise data science center of excellence, built analytics capability through structured learning programs, and implemented version control standards serving cross-functional teams.
Enterprise Broker Analytics Leadership: Led broker segmentation transformation using advanced models incorporating MSA data, Census demographics, and economic indicators across 15 regional markets, achieving 22% retention improvement and $2.1M incremental revenue.

Senior Manager II – Consumer & Market Analytics

Walmart Inc.

Bentonville, AR

2023 - 2022

Geographic Revenue Analytics: Deployed XGBoost and econometric models analyzing consumer shopping patterns across 4,000+ stores using demographic clustering and geographic data to optimize apparel assortment, improving inventory turns by 12% through precision demand forecasting.
Customer Segmentation at Scale: Developed enterprise clustering application combining purchase behavior and geographic characteristics to automate store-level assortment recommendations, saving 80+ analyst hours monthly and reducing stockouts by 15%.
Consumer Journey Optimization: Established behavioral cohort analysis framework integrating Amplitude engagement metrics with FullStory web sessions, enabling merchandising optimizations that increased conversion rates by 18% in target demographics.
Dynamic Pricing & Promotional Analytics: Built econometric models incorporating regional economic indicators and competitor pricing data to enable dynamic pricing strategies, improving gross margin by 2.3% while maintaining market competitiveness.
Cloud Infrastructure & Optimization: Architected automated data pipeline using GCP and BigQuery processing 50M+ customer transactions daily, reducing manual data preparation by 50% and achieving $1M annual cost savings.

Applied Economist and Demand Analytics Manager

Shaw Industries Inc.

Dalton, GA

2022 - 2019

Demand Forecasting with Economic Integration: Modernized forecasting models integrating housing market data, construction permits, and Census demographic trends using advanced anomaly detection algorithms, achieving $24M inventory optimization through 35% improvement in demand prediction accuracy.
Customer Preference Analytics: Designed decision tree and clustering models analyzing out-of-stock scenarios by MSA and household income segments, developing recommendation engine with 84% acceptance rate for alternative product suggestions.
Geographic Market Intelligence: Led comprehensive analysis of flooring demand patterns across 180+ MSAs, incorporating income demographics and local economic conditions to optimize regional inventory allocation and reduce stockouts by 28%.
Data Engineering & Demographic Enrichment: Implemented dbt framework transforming raw customer transaction data into analytics-ready tables in Snowflake, standardizing geographic and demographic enrichment processes, reducing analysis preparation time by 35%.
Cross-functional Analytics Leadership: Built and managed team of 6 data scientists specializing in customer behavior analysis, established best practices for econometric modeling and customer segmentation.

Decision Support Analyst

McKee Foods Corporation

Collegedale, TN

2019 - 2016

Predictive Customer Modeling: Led team building econometric models predicting customer purchase behavior across regional markets, incorporating demographic and economic variables to optimize product placement and promotional strategies.
Executive Analytics Infrastructure: Designed interactive Power BI dashboards integrating customer transaction data with Workday demographic information, providing C-suite real-time insights into customer segment performance.
Data Pipeline Architecture: Implemented ETL processes in Alteryx automating customer data aggregation from multiple sources, ensuring data quality and enabling consistent demographic and geographic enrichment.
Self-Service Analytics: Administered Tableau Server deployment enabling business users to conduct independent customer analysis, fostering data-driven culture and reducing ad-hoc reporting requests by 40%.

Graduate Research Assistant

College of Agricultural and Environmental Sciences, The University of Georgia